However, because of limited GPU memory , we only use the highest level feature \(F^{(n)}_{6}\)  (\(8\times 8\)  resolution), which contains less detailed information, to model group-wise relationships. To make final predicted co-saliency maps contain more detailed information, we first use a simple U-shaped architecture that enables feature aggregation at low-level via skip-connections. Moreover, inspired by BASNet [1]}, which uses pixel-level, region-level and object-level supervision strategy to predict the salient objects with fine structures and clear boundaries, we also use \(SSIM\) 
and \(F_m\)  losses in addition to BCE loss. The results can be seen in Table.REF . A better performance has been achieved through the combination of \(BCE\) , \(SSIM\)  and \(F_m\) . The work [2]} addresses that predicted co-saliency maps with fine boundaries is one of future directions, and we try to address this problem by simply using the \(SSIM\)  and \(F_m\)  losses in this paper. While the main contributions of this paper are the proposed CoSformer framework and contrast learning scheme.
